A Sparse Potential-Data-Driven Computational Method for One-Dimensional Electro-Osmotic Consolidation

The classical one-dimensional electroosmotic consolidation theory assumes a linear potential distribution independent of time, thereby neglecting the influence of spatiotemporal evolution of soil electrical properties on the consolidation process. To address this limitation, a one-dimensional electroosmotic model test is conducted in this study, revealing the spatiotemporal evolution characteristics of the electric potential field. The results indicate that changes in electrode-soil interfacial properties and the nonuniform evolution of soil electrical parameters are the primary factors responsible for the redistribution of the potential field. On this basis, the Physics-Informed Neural Networks (PINNs) method is introduced for potential field reconstruction, fully leveraging the combination of physical constraints and data-driven approaches to achieve spatial densification of the potential field under sparse observations. Furthermore, a surrogate model characterizing the spatiotemporal evolution of the potential field is constructed and embedded into the governing equation of electroosmotic consolidation as a nonhomogeneous source term, which is then solved numerically using the finite difference method. The computational results demonstrate that the negative excess pore water pressures obtained by the proposed method exhibit smaller magnitudes than those predicted by the classical theory, with the discrepancies primarily concentrated near the anode, reaching a maximum of approximately 120 kPa. The proposed approach provides a feasible pathway for electroosmotic consolidation analysis that accounts for the spatiotemporal evolution of the electric potential field.